The application of an artificial intelligence tool to improve diabetic ketoacidosis treatment security in the emergency department: a quasi-experimental study
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BACKGROUND
Diabetic ketoacidosis (DKA) is one of the most serious diabetes complications. It is
characterized by hyperglycemia, anion gap metabolic acidosis and increased total body
ketone concentration. DKA is the most common cause of death in youth type 1 diabetic
patients and an important cause of morbimortality in type 2 diabetic patients. Its
management is complex, however, if it is well performed DKA has a good prognosis.
Lately, the need to standardize DKA treatment has become an important issue in our
Department.
OBJECTIVE
Our main objective is to decrease hospital length of stay of DKA patients treated in
Hospital Universitari Josep Trueta’s Emergency Department (ED) with the application of
a computer decision support and electronic order sets (CDS&EOS) to minimize security
errors.
DESIGN
Quasi-experimental study designed as a before-and-after evaluation of the application
of a CDS&EOS in ED’s SILICON® to standardize DKA management.
PARTICIPANTS
A consecutive non-probabilistic model will be used to select DKA patients aged 18 years
old or older treated in Hospital Universitari Josep Trueta’s ED.
METHODS
The study will include 134 participants in total, 67 for each group (pre and post-
intervention). Each sample will be selected in an 18-month period, with a washout
period between them. Data will be collected prospectively between May 2021 and
October 2024. The association between the independent and dependent variables will
be adjusted to avoid possible confounding factors